Detecting potential safety issues in clinical trials by Bayesian screening.
Patients in large clinical trials report many different adverse events, most of which will not have been anticipated in the protocol. Conventional hypothesis testing of between group differences for each adverse event can be problematic: Lack of significance does not mean lack of risk, the tests usually are not adjusted for multiplicity, and the data determine which hypotheses are tested. This paper describes a Bayesian screening approach that does not test hypotheses, is self-adjusting for multiplicity, provides a direct assessment of the likelihood of no material drug-event association, and quantifies the strength of the observed association. The approach directly incorporates clinical judgment by having the criteria for treatment association determined by the investigator(s). Diagnostic properties can be evaluated analytically. Application of the method to findings from a vaccine trial yield results similar to those found by methods using a false discovery rate argument and using a hierarchical Bayes approach.
Gould
A Lawrence
AL
Merck Research Laboratories, UG1D-88, 351 North Sumneytown Pike, North Wales, PA 19454, USA. larry_gould@merck.com
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000009:adverse effects* / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D011336:Probability; D012449:Safety / Q000706:statistics & numerical data